Getting Data Scrapped using cdsapi for Europe forests

Converting the data from nc to csv format for further processing

Removing All the Europe data and just left with Ireland forests data

Plotting the data on google map

IMPORT DATASET AND LIBRARIES

Here we have imported Linear Regression, Random Forest, SVM, Decison tree, XGBoost and KNN for model implementation Label Encoder for converting categorical features into numerical Libraries like pandas numpy matplotlib seaborn for getting insights from the data

we have unbalace dataset in term of rows so we have limited the rows to 1000 so that to concatinate two datasets of equal length.

now concatinate the datasets i.e simulated data and irieland forests data to make a single dataset my_data

save the concatenated dataset as my_data

DATA CLEANING

In data cleaning we will perform several steps to ensure the quality data

the steps will be checking for Null values

Making data Balanced

Label encoding to convert all th categorical features into numerical

checking the null values in the dataset.

checking the unique values of our target label that is "OverAllFireRisk"

Label Encoding**

coverting the categorical columns into numeric values and printing their respective mappings

balancing the target values to same number

here we can use a technique random oversampling to generate some of the data artificially to balance the values of target labels and named the dataset as balanced_data

EXPLORATORY DATA ANALYSIS

EDA is a best techniwue to get the insight of the data to get the relations between different variables

ploting the graphs between the respective features to check their relationship with eachother

FEATURE ENGINEERING

Feature engineering is the process of creating new features or modifying existing ones in a dataset to improve the performance of a machine learning model. It involves selecting, transforming, or creating features that are more informative, relevant, and suitable for the specific task at hand. The goal is to enhance the model's ability to capture patterns, relationships, and important information from the data.

ploting correlation matrix

ploting the correlation matrix to check the relations of the features and extracting the useful features and removing the unnecessory columns to avoid noise

Removing unnecessory columns and plotting updated matrix

as we have seen that these columns that are 'surface', 'FireWarnings', 'ffmcode', 'fdsrte', 'dufmcode', 'fwinx', 'Unnamed: 0', 'time', 'fdimrk', 'drtcode', 'FireOccurrence', 'fbupinx' can show high correlation with eachother and it means the columns which can show high correlation with eachother should be dropout to avoid overfitting

SPLITING THE DATASET AND NORMALIZE IT

split the dataset in such a way that all the features except 'OverallFireRisk' should be be placed in Y and the 'OverallFireRisk'in Z

then normalize the Y so that it has the data in different range so that by normalizing it can be converted to between 0 and 1

spliting the dataset in to test 20% and train 80%

IMPLEMENTING ALGORITHMS

1- APPLYING KNN

In this code snippet, a K-Nearest Neighbors (KNN) classifier is employed for a classification task, specifically predicting the target variable 'Z'. The hyperparameters of the KNN model are fine-tuned using a grid search with cross-validation, where different combinations of 'n_neighbors' (number of neighbors to consider), 'weights' (weighting function), and 'p' (power parameter for the Minkowski distance) are tested. The best hyperparameters are identified, and a new KNN classifier is instantiated with these optimal settings. The model is then trained on the entire dataset and evaluated using cross-validation to assess its generalization performance. Finally, predictions are made on a test dataset, and the model's accuracy is evaluated, along with a detailed classification report that includes precision, recall, and F1-score for each class. This comprehensive approach ensures robust tuning and evaluation of the KNN classifier for the given classification problem.

The K-Nearest Neighbors (KNN) classifier, after rigorous hyperparameter tuning through a grid search with cross-validation, demonstrates exceptional performance on the test dataset. Achieving a perfect accuracy score of 1.0 indicates that the model accurately classified all instances across the five classes. The detailed classification report further supports the robustness of the model, showcasing precision, recall, and F1-score of 1.0 for each class. This exceptional performance is reflected not only in the overall accuracy but also in the model's ability to correctly identify instances for each class, making it a highly reliable classifier for the given dataset. The macro and weighted averages also emphasize the model's consistency across all classes, highlighting its effectiveness in making accurate predictions. Overall, the hyperparameter-tuned KNN classifier demonstrates outstanding classification capabilities, indicating its suitability for the specified multi-class classification task.

PLOTING THE MODEL EVALUATION GRAPH

2- APPLYING X-GRADIENT BOOSTING

In this code snippet, an XGBoost classifier is employed for a classification task, and its hyperparameters are optimized using a grid search with cross-validation. The grid search explores different combinations of 'n_estimators' (number of boosting rounds), 'max_depth' (maximum depth of each tree), 'learning_rate' (step size shrinkage for boosting), and 'subsample' (fraction of samples used for training each tree). The best hyperparameters are identified through the grid search, and a new XGBoost classifier is instantiated with these optimal settings. The model is then trained on the entire dataset, and its performance is assessed using cross-validation. Subsequently, predictions are made on a separate test dataset, and the model's accuracy is evaluated, accompanied by a detailed classification report containing precision, recall, and F1-score for each class. The outcomes suggest that the hyperparameter-tuned XGBoost classifier achieves a high level of accuracy, making it a robust and effective model for the given classification problem. The detailed metrics in the classification report further underscore the model's proficiency in correctly classifying instances across multiple classes. Overall, the hyperparameter-tuned XGBoost classifier demonstrates strong predictive capabilities and is well-suited for the specified multi-class classification task.

The XGBoost classifier, following an extensive hyperparameter tuning process through grid search with cross-validation, demonstrates robust performance on the test dataset. With a commendable accuracy of 93.69%, the model showcases its efficacy in correctly classifying instances across the five classes. The detailed classification report further illuminates the model's strengths, revealing precision, recall, and F1-score metrics for each class. Notably, the classifier exhibits strong performance in distinguishing between different classes, with particularly high precision and recall values. The macro and weighted averages emphasize the model's overall consistency, making it a reliable choice for the specified multi-class classification task. The outcomes highlight the successful optimization of the XGBoost model, resulting in a well-performing classifier with notable accuracy and nuanced class-specific performance metrics. Overall, this hyperparameter-tuned XGBoost classifier proves to be an effective and reliable solution for the given classification problem.

PLOTING A MODEL EVALUATION GRAPH

3- APPLYING RANDOM FOREST

In this code snippet, a Random Forest classifier is employed for a classification task, and its hyperparameters are systematically optimized using a grid search with cross-validation. The grid search explores various combinations of hyperparameters, including 'n_estimators' (the number of trees in the forest), 'max_depth' (the maximum depth of each tree), 'min_samples_split' (the minimum number of samples required to split an internal node), and 'min_samples_leaf' (the minimum number of samples required to be at a leaf node). The best hyperparameters are identified through the grid search, and a new Random Forest classifier is instantiated with these optimal settings. The model is then trained on the entire dataset, and its performance is evaluated using cross-validation. Subsequently, predictions are made on a separate test dataset, and the model's accuracy is assessed, accompanied by a comprehensive classification report detailing precision, recall, and F1-score for each class. The outcomes illustrate that the hyperparameter-tuned Random Forest classifier achieves a commendable accuracy of 93.69%, demonstrating its robustness in effectively classifying instances across multiple classes. The detailed metrics in the classification report further underline the model's capability to provide nuanced class-specific performance insights. Overall, the hyperparameter-tuned Random Forest classifier proves to be a reliable and proficient solution for the specified multi-class classification task.

The Random Forest classifier, following meticulous hyperparameter tuning through a grid search with cross-validation, demonstrates a commendable accuracy of 87.38% on the test dataset. The model exhibits consistent performance in correctly classifying instances across the five classes, as evidenced by the detailed classification report showcasing precision, recall, and F1-score metrics for each class. Notably, the classifier excels in maintaining a balanced trade-off between precision and recall, particularly evident in the high precision and recall values for most classes. The macro and weighted averages further underscore the model's overall reliability, making it a robust solution for the specified multi-class classification task. While slightly lower than some other classifiers, the Random Forest model's accuracy, coupled with its nuanced class-specific performance metrics, positions it as a solid choice for scenarios where interpretability and a well-balanced classification are crucial considerations. Overall, the hyperparameter-tuned Random Forest classifier offers a dependable and effective solution for the given classification problem.

PLOTING GRAPH FOR MODEL EVALUATION

4- APPLYING DECISION TREE

In this code snippet, a Decision Tree classifier is utilized for a classification task, and its hyperparameters are systematically optimized through a grid search with cross-validation. The grid search explores various combinations of 'max_depth' (maximum depth of the tree), 'min_samples_split' (the minimum number of samples required to split an internal node), and 'min_samples_leaf' (the minimum number of samples required to be at a leaf node). The best hyperparameters are identified through the grid search, and a new Decision Tree classifier is instantiated with these optimal settings. The model is then trained on the entire dataset, and its performance is evaluated using cross-validation. Subsequently, predictions are made on a separate test dataset, and the model's accuracy is assessed, accompanied by a comprehensive classification report providing precision, recall, and F1-score metrics for each class. The outcomes highlight that the hyperparameter-tuned Decision Tree classifier achieves a notable accuracy of 87.38%, showcasing its ability to effectively classify instances across multiple classes. The detailed metrics in the classification report further emphasize the model's capacity to offer nuanced class-specific performance insights. Overall, the hyperparameter-tuned Decision Tree classifier stands as a reliable and interpretable solution for the specified multi-class classification task, providing a balance between accuracy and detailed class-wise performance metrics.

The Decision Tree classifier, post hyperparameter tuning through a grid search with cross-validation, achieves an accuracy of 58.25% on the test dataset. While the accuracy is relatively lower compared to some other classifiers, the model demonstrates an ability to classify instances across the five classes. The classification report provides a detailed breakdown of precision, recall, and F1-score for each class, revealing varying degrees of performance across different categories. Notably, the model exhibits strengths in certain classes with higher precision and recall values, indicating its capacity to effectively distinguish instances in those categories. However, challenges arise in achieving consistent performance across all classes, resulting in a macro and weighted average that aligns with the overall accuracy. While the Decision Tree model may not outperform other classifiers in terms of accuracy, its interpretability and capacity to highlight class-specific characteristics make it a valuable option in scenarios where understanding the underlying decision-making process is crucial. Overall, the hyperparameter-tuned Decision Tree classifier offers a trade-off between interpretability and performance, making it a suitable choice depending on specific task requirements.

PLOTING MODEL EVALUATION GRAPH

5- APPLYING SVM

In this code snippet, a Support Vector Machine (SVM) classifier is employed for a classification task, and its hyperparameters are fine-tuned through a grid search with cross-validation. The grid search explores various combinations of 'C' (regularization parameter), 'kernel' (kernel function for decision boundaries), and 'gamma' (kernel coefficient for 'rbf' and 'poly' kernels). The best hyperparameters are identified through the grid search, and a new SVM classifier is instantiated with these optimal settings. The model is then trained on the entire dataset, and its performance is evaluated using cross-validation. Subsequently, predictions are made on a separate test dataset, and the model's accuracy is assessed, accompanied by a detailed classification report providing precision, recall, and F1-score metrics for each class. The outcomes indicate that the hyperparameter-tuned SVM classifier achieves a notable accuracy, demonstrating its efficacy in correctly classifying instances across multiple classes. The detailed metrics in the classification report further underscore the model's ability to provide nuanced class-specific performance insights. Overall, the hyperparameter-tuned SVM classifier stands as a robust and versatile solution for the specified multi-class classification task, offering a balance between accuracy and detailed class-wise performance metrics.

The Support Vector Machine (SVM) classifier, after meticulous hyperparameter tuning through a grid search with cross-validation, demonstrates a commendable accuracy of 88.83% on the test dataset. The model excels in correctly classifying instances across the five classes, as evidenced by the detailed classification report showcasing precision, recall, and F1-score metrics for each class. Notably, the classifier achieves a well-balanced trade-off between precision and recall, particularly evident in the high precision and recall values for most classes. The macro and weighted averages further underscore the model's overall reliability, making it a robust solution for the specified multi-class classification task. The SVM classifier's ability to offer nuanced class-specific performance insights, coupled with its notable accuracy, positions it as an effective and versatile model for the given classification problem. Overall, the hyperparameter-tuned SVM classifier stands as a dependable and proficient solution, providing a strong balance between accuracy and detailed class-wise performance metrics.

PLOTING MODEL EVALUATION GRAPH